{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "73ztBH5yK_bS"
      },
      "source": [
        "# Multiple changepoint detection and Bayesian model selection\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "n5om5yiB_zvF"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\"); { display-mode: \"form\" }\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Qianaf6u_7G_"
      },
      "source": [
        "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "  \u003ctd\u003e\n",
        "    \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/jupyter_notebooks/Multiple_changepoint_detection_and_Bayesian_model_selection.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
        "  \u003c/td\u003e\n",
        "\u003c/table\u003e"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-o9zA5TO_-hx"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "No2QPkJ1_9z9"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_probability as tfp\n",
        "from tensorflow_probability import distributions as tfd\n",
        "\n",
        "from matplotlib import pylab as plt\n",
        "%matplotlib inline\n",
        "import scipy.stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "UoIGcwDcLK8s"
      },
      "source": [
        "##  Task: changepoint detection with multiple changepoints"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MkPCuGGp464l"
      },
      "source": [
        "Consider a changepoint detection task:  events happen at a rate that changes over time, driven by sudden shifts in the (unobserved) state of some system or process generating the data.\n",
        "\n",
        "For example, we might observe a series of counts like the following:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 297
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 609,
          "status": "ok",
          "timestamp": 1549408856555,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "kmk8w7-vuKSm",
        "outputId": "7565a823-ab4a-4e53-b643-a077a69ca804"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[\u003cmatplotlib.lines.Line2D at 0x7fac2a1e1550\u003e]"
            ]
          },
          "execution_count": 3,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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5cOZidoJ9tK036nGzxTbCMvzkdfWQJ352gs8W0Y06JUw6FZV0CCLfsOna2HIOo6lWj2qz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\niUCCTxAEUSKQ4BMEQZQIJPgEQRAlwv8PXO8RCzfRtb8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fac2a1f8810\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "true_rates = [40, 3, 20, 50]\n",
        "true_durations = [10, 20, 5, 35]\n",
        "\n",
        "observed_counts = np.concatenate([\n",
        "  scipy.stats.poisson(rate).rvs(num_steps)\n",
        "    for (rate, num_steps) in zip(true_rates, true_durations)\n",
        "]).astype(np.float32)\n",
        "\n",
        "plt.plot(observed_counts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "TWx9cuas0EcE"
      },
      "source": [
        "These could represent the number of failures in a datacenter, number of visitors to a webpage, number of packets on a network link, etc.\n",
        "\n",
        "Note it's not entirely apparent how many distinct system regimes there are just from looking at the data. Can you tell where each of the three switchpoints occurs?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "09nB0iTzky85"
      },
      "source": [
        "## Known number of states\n",
        "\n",
        "We'll first consider the (perhaps unrealistic) case where the number of unobserved states is known a priori. Here, we'd assume we know there are four latent states.\n",
        "\n",
        "We model this problem as a switching (inhomogeneous) Poisson process: at each point in time, the number of events that occur is Poisson distributed, and the *rate* of events is determined by the unobserved system state $z_t$:\n",
        "\n",
        "$$x_t \\sim \\text{Poisson}(\\lambda_{z_t})$$\n",
        "\n",
        "The latent states are discrete: $z_t \\in \\{1, 2, 3, 4\\}$, so $\\lambda = [\\lambda_1, \\lambda_2, \\lambda_3, \\lambda_4]$ is a simple vector containing a Poisson rate for each state. To model the evolution of states over time, we'll define a simple transition model $p(z_t | z_{t-1})$: let's say that at each step we stay in the previous state with some probability $p$, and with probability $1-p$ we transition to a different state uniformly at random. The initial state is also chosen uniformly at random, so we have:\n",
        "\n",
        "$$\n",
        "\\begin{align*}\n",
        "z_1 \u0026\\sim \\text{Categorical}\\left(\\left\\{\\frac{1}{4}, \\frac{1}{4}, \\frac{1}{4}, \\frac{1}{4}\\right\\}\\right)\\\\\n",
        "z_t | z_{t-1} \u0026\\sim \\text{Categorical}\\left(\\left\\{\\begin{array}{cc}p \u0026 \\text{if } z_t = z_{t-1} \\\\ \\frac{1-p}{4-1} \u0026 \\text{otherwise}\\end{array}\\right\\}\\right)\n",
        "\\end{align*}$$\n",
        "\n",
        "These assumptions correspond to a [hidden Markov model](http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf) with Poisson emissions. We can encode them in TFP using `tfd.HiddenMarkovModel`. First, we define the transition matrix and the uniform prior on the initial state:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 145
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 335,
          "status": "ok",
          "timestamp": 1549408860866,
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            "displayName": "",
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          },
          "user_tz": 480
        },
        "id": "0qs_l4p4nygq",
        "outputId": "f184ef24-80b3-4bb8-d05b-7a4b9feae611"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Initial state logits:\n",
            "[ 0.  0.  0.  0.]\n",
            "Transition matrix:\n",
            "[[ 0.94999999  0.01666667  0.01666667  0.01666667]\n",
            " [ 0.01666667  0.94999999  0.01666667  0.01666667]\n",
            " [ 0.01666667  0.01666667  0.94999999  0.01666667]\n",
            " [ 0.01666667  0.01666667  0.01666667  0.94999999]]\n"
          ]
        }
      ],
      "source": [
        "num_states = 4\n",
        "\n",
        "initial_state_logits = np.zeros([num_states], dtype=np.float32) # uniform distribution\n",
        "\n",
        "daily_change_prob = 0.05\n",
        "transition_probs = daily_change_prob / (num_states-1) * np.ones(\n",
        "    [num_states, num_states], dtype=np.float32)\n",
        "np.fill_diagonal(transition_probs,\n",
        "                 1-daily_change_prob)\n",
        "\n",
        "print(\"Initial state logits:\\n{}\".format(initial_state_logits))\n",
        "print(\"Transition matrix:\\n{}\".format(transition_probs))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vWshnDRepxaT"
      },
      "source": [
        "Next, we build a `tfd.HiddenMarkovModel` distribution, using a trainable variable to represent the rates associated with each system state. We parameterize the rates in log-space to ensure they are positive-valued."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "bvEpqBxvoleY"
      },
      "outputs": [],
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "# Define variable to represent the unknown log rates.\n",
        "trainable_rates = tf.exp(tf.get_variable(\n",
        "    'log_rates', initializer=(\n",
        "        np.log(np.mean(observed_counts)) + tf.random_normal([num_states]))))\n",
        "\n",
        "hmm = tfd.HiddenMarkovModel(\n",
        "  initial_distribution=tfd.Categorical(\n",
        "      logits=initial_state_logits),\n",
        "  transition_distribution=tfd.Categorical(probs=transition_probs),\n",
        "  observation_distribution=tfd.Poisson(trainable_rates),\n",
        "  num_steps=len(observed_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4JA6D9EsqNTe"
      },
      "source": [
        "Finally, we define the model's total log density, including a weakly-informative LogNormal prior on the rates, and run an optimizer to compute the [maximum a posteriori](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation) (MAP) fit to the observed count data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "6mirKxnNqJSu"
      },
      "outputs": [],
      "source": [
        "rate_prior = tfd.LogNormal(5, 5)\n",
        "total_log_prob = (\n",
        "    tf.reduce_sum(rate_prior.log_prob(trainable_rates)) +\n",
        "    hmm.log_prob(observed_counts))\n",
        "\n",
        "train_op = tf.train.AdamOptimizer(0.1).minimize(-total_log_prob)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 254
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 2360,
          "status": "ok",
          "timestamp": 1549409096512,
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            "displayName": "",
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            "userId": ""
          },
          "user_tz": 480
        },
        "id": "gSjyTtkDrOHu",
        "outputId": "7bac9b27-ada8-403a-ebc8-105236c27716"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step 0: log prob -656.555480957 rates [  73.95337677   28.91309357  115.55673218   25.37959099]\n",
            "step 20: log prob -275.879882812 rates [ 33.17310715  76.04164886  52.57240295   5.49490452]\n",
            "step 40: log prob -261.123962402 rates [ 22.35352135  81.7260437   51.71455383   3.3429575 ]\n",
            "step 60: log prob -260.758117676 rates [ 20.29379463  77.85356903  51.83719635   3.51979518]\n",
            "step 80: log prob -260.13458252 rates [ 21.70909691  71.68695831  51.71755219   3.8770504 ]\n",
            "step 100: log prob -258.179626465 rates [ 21.58044052  59.14146042  50.63894653   3.90205407]\n",
            "step 120: log prob -248.42401123 rates [ 20.82899475  54.13412857  41.92335129   3.85124207]\n",
            "step 140: log prob -248.51461792 rates [ 20.76467514  53.67557526  40.03132248   3.85271001]\n",
            "step 160: log prob -248.346206665 rates [ 20.84091568  54.12538147  41.20120239   3.85899258]\n",
            "step 180: log prob -248.344451904 rates [ 20.80753136  54.14757919  41.08810806   3.85737729]\n",
            "step 200: log prob -248.343658447 rates [ 20.81398201  54.12064362  41.01148605   3.85704994]\n",
            "Inferred rates: [ 20.81398201  54.12064362  41.01148605   3.85704994]\n",
            "True rates: [40, 3, 20, 50]\n"
          ]
        }
      ],
      "source": [
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(201):\n",
        "  _, loss_, rates_ = sess.run((train_op, total_log_prob, trainable_rates))\n",
        "\n",
        "  if step % 20 == 0:\n",
        "    print(\"step {}: log prob {} rates {}\".format(step, loss_, rates_))\n",
        "\n",
        "print(\"Inferred rates: {}\".format(rates_))\n",
        "print(\"True rates: {}\".format(true_rates))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "9kGRv8gwrtP5"
      },
      "source": [
        "It worked! Note that the latent states in this model are identifiable only up to permutation, so the rates we recovered are in a different order, and there's a bit of noise, but generally they match pretty well."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "43AfcMTjvs7a"
      },
      "source": [
        "### Recovering the state trajectory\n",
        "\n",
        "Now that we've fit the model, we might want to reconstruct *which* state the model believes the system was in at each timestep.\n",
        "\n",
        "This is a *posterior inference* task: given the observed counts $x_{1:T}$ and model parameters (rates) $\\lambda$, we want to infer the sequence of discrete latent variables, following the posterior distribution $p(z_{1:T} | x_{1:T}, \\lambda)$. In a hidden Markov model, we can efficiently compute marginals and other properties of this distribution using standard message-passing algorithms. In particular, the `posterior_marginals` method will efficiently compute (using the [forward-backward algorithm](https://en.wikipedia.org/wiki/Forward%E2%80%93backward_algorithm)) the marginal probability distribution $p(Z_t = z_t | x_{1:T})$ over the discrete latent state $Z_t$ at each timestep $t$."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "IpTbdyah-IyX"
      },
      "outputs": [],
      "source": [
        "# Runs forward-backward algorithm to compute marginal posteriors.\n",
        "posterior_dists = hmm.posterior_marginals(observed_counts)\n",
        "posterior_probs_ = sess.run(posterior_dists.probs_parameter())  # extract the probabilities."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "8JsA-nG-OpKT"
      },
      "outputs": [],
      "source": [
        "sess.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "cOYMlvssFDwx"
      },
      "source": [
        "Plotting the posterior probabilities, we recover the model's \"explanation\" of the data: at which points in time is each state active?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 728
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 1913,
          "status": "ok",
          "timestamp": 1549410463982,
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        "id": "oZ7C937t-Xh3",
        "outputId": "e8c92f4f-879c-45f4-dd1f-2c6c3ff2baa2"
      },
      "outputs": [
        {
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bXq9n1PE0dje1rgV1kM1IpwsnPoI84tSmakqhUCyhlslqdgbVFAJkL1iWhdvt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\nXDCZjNBo1P0iWEEOMkH0gJaWFuzatQttbW25NqXP4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88FAIwdOxbFxcUwGAxoamoCYwwq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\nif8H/8TAfzyDEUYAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fac2315f410\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_state_posterior(ax, state_posterior_probs, title):\n",
        "  ln1 = ax.plot(state_posterior_probs, c='blue', lw=3, label='p(state | counts)')\n",
        "  ax.set_ylim(0., 1.1)\n",
        "  ax.set_ylabel('posterior probability')\n",
        "  ax2 = ax.twinx()\n",
        "  ln2 = ax2.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "  ax2.set_title(title)\n",
        "  ax2.set_xlabel(\"time\")\n",
        "  lns = ln1+ln2\n",
        "  labs = [l.get_label() for l in lns]\n",
        "  ax.legend(lns, labs, loc=4)\n",
        "  ax.grid(True, color='white')\n",
        "  ax2.grid(False)\n",
        "\n",
        "fig = plt.figure(figsize=(10, 10))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 1),\n",
        "                     posterior_probs_[:, 0],\n",
        "                     title=\"state 0 (rate {:.2f})\".format(rates_[0]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 2),\n",
        "                     posterior_probs_[:, 1],\n",
        "                     title=\"state 1 (rate {:.2f})\".format(rates_[1]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 3),\n",
        "                     posterior_probs_[:, 2],\n",
        "                     title=\"state 2 (rate {:.2f})\".format(rates_[2]))\n",
        "plot_state_posterior(fig.add_subplot(2, 2, 4),\n",
        "                     posterior_probs_[:, 3],\n",
        "                     title=\"state 3 (rate {:.2f})\".format(rates_[3]))\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_QhFHJ01NPVj"
      },
      "source": [
        "In this (simple) case, we see that the model is usually quite confident: at most timesteps it assigns essentially all probability mass to a single one of the four states. Luckily, the explanations look reasonable!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "92psCOwMGiQp"
      },
      "source": [
        "We can also visualize this posterior in terms of the rate associated with the *most likely* latent state at each timestep, condensing the probabilistic posterior into a single explanation:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PsXpBrH3DKbl"
      },
      "outputs": [],
      "source": [
        "most_probable_states = np.argmax(posterior_probs_, axis=1)\n",
        "most_probable_rates = rates_[most_probable_states]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 325
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 543,
          "status": "ok",
          "timestamp": 1549413247546,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "CCIwVTnyOcsW",
        "outputId": "f5cae125-0d11-4d42-9630-4fc07e59786f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.legend.Legend at 0x7fac2581a610\u003e"
            ]
          },
          "execution_count": 68,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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y1NVNEtW7LddZNgDCdoBipki9Xm/KDe9zUUEqpnJULJ2uDEqlMqvtusZCqvVs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\no/XUowAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fac23491ed0\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(10, 4))\n",
        "ax = fig.add_subplot(1, 1, 1)\n",
        "ax.plot(most_probable_rates, c='green', lw=3, label='inferred rate')\n",
        "ax.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "ax.set_ylabel(\"latent rate\")\n",
        "ax.set_xlabel(\"time\")\n",
        "ax.set_title(\"Inferred latent rate over time\")\n",
        "ax.legend(loc=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4MhfH3a4OBGV"
      },
      "source": [
        "Technical note: instead of the most probable state at each individual timestep, $z^*_t = \\text{argmax}_{z_t} p(z_t | x_{1:T})$, we could have asked for the most probable latent *trajectory*, $z^* = \\text{argmax}_z p(z | x_{1:T})$ (or even samples from the posterior over trajectories!), taking dependence between timesteps into account. To illustrate the difference, suppose a rock-paper-scissors player plays rock 40% of the time, but never twice in a row: rock may be the most likely marginal state at every point in time, but \"rock, rock, rock...'' is definitely *not* the most likely trajectory -- in fact, it has zero probability!\n",
        "\n",
        "TODO(davmre): once `tfp.HiddenMarkovModel` implements the [Viterbi algorithm](https://en.wikipedia.org/wiki/Viterbi_algorithm) to find highest-probability trajectories, update this section to use it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "7ytq0tN7tteU"
      },
      "source": [
        "## Unknown number of states\n",
        "\n",
        "In real problems, we may not know the 'true' number of states in the system we're modeling. This may not always be a concern: if you don't particularly care about the identities of the unknown states, you could just run a model with more states than you know the model will need, and learn (something like) a bunch of duplicate copies of the actual states. But let's assume you do care about inferring the 'true' number of latent states.\n",
        "\n",
        "We can view this as a case of [Bayesian model selection](http://alumni.media.mit.edu/~tpminka/statlearn/demo/): we have a set of candidate models, each with a different number of latent states, and we want to choose the one that is most likely to have generated the observed data. To do this, we compute the marginal likelihood of the data under each model (we could also add a prior on the models themselves, but that won't be necessary in this analysis; the [Bayesian Occam's razor](https://www.cs.princeton.edu/courses/archive/fall09/cos597A/papers/MacKay2003-Ch28.pdf) turns out to be sufficient to encode a preference towards simpler models).\n",
        "\n",
        "Unfortunately, the true marginal likelihood, which integrates over both the discrete states $z_{1:T}$ and the (vector of) rate parameters $\\lambda$, $$p(x_{1:T}) = \\int p(x_{1:T}, z_{1:T}, \\lambda) dz d\\lambda,$$ is not tractable for this model. For convenience, we'll approximate it using a so-called \"[empirical Bayes](https://www.cs.ubc.ca/~schmidtm/Courses/540-W16/L19.pdf)\" or \"type II maximum likelihood\" estimate: instead of fully integrating out the (unknown) rate parameters $\\lambda$ associated with each system state, we'll optimize over their values:\n",
        "\n",
        "$$\\tilde{p}(x_{1:T}) = \\max_\\lambda \\int p(x_{1:T}, z_{1:T}, \\lambda) dz$$\n",
        "\n",
        "This approximation may overfit, i.e., it will prefer more complex models than the true marginal likelihood would. We could consider more faithful approximations, e.g., optimizing a variational lower bound, or using a Monte Carlo estimator such as [annealed importance sampling](https://www.tensorflow.org/probability/api_docs/python/tfp/mcmc/sample_annealed_importance_chain); these are (sadly) beyond the scope of this notebook. (For more on Bayesian model selection and approximations, chapter 7 of the excellent [Machine Learning: a Probabilistic Perspective\n",
        "](https://www.cs.ubc.ca/~murphyk/MLbook/) is a good reference.)\n",
        "\n",
        "In principle, we could do this model comparison simply by rerunning the optimization above many times with different values of `num_states`, but that would be a lot of work. Here we'll show how to consider multiple models in parallel, using TFP's `batch_shape` mechanism for vectorization."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dtClNe6fyZAD"
      },
      "source": [
        "**Transition matrix and initial state prior**: rather than building a single model description, now we'll build a *batch* of transition matrices and prior logits, one for each candidate model up to `max_num_states`. For easy batching we'll need to ensure that all computations have the same 'shape': this must correspond to the dimensions of the largest model we'll fit. To handle smaller models, we can  'embed' their descriptions in the topmost dimensions of the state space, effectively treating the remaining dimensions as dummy states that are never used."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 472
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 330,
          "status": "ok",
          "timestamp": 1549416751780,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "vqyTuY5hrmdR",
        "outputId": "e8b3f7b1-0f7b-4873-b973-408963b4cd86"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of initial_state_logits: (10, 10)\n",
            "Shape of transition probs: (10, 10, 10)\n",
            "Example initial state logits for num_states==3:\n",
            "[   0.    0.    0. -100. -100. -100. -100. -100. -100. -100.]\n",
            "Example transition_probs for num_states==3:\n",
            "[[ 0.94999999  0.025       0.025       0.          0.          0.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.025       0.94999999  0.025       0.          0.          0.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.025       0.025       0.94999999  0.          0.          0.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.          0.          0.          1.          0.          0.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.          0.          0.          0.          1.          0.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.          0.          0.          0.          0.          1.          0.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.          0.          0.          0.          0.          0.          1.\n",
            "   0.          0.          0.        ]\n",
            " [ 0.          0.          0.          0.          0.          0.          0.\n",
            "   1.          0.          0.        ]\n",
            " [ 0.          0.          0.          0.          0.          0.          0.\n",
            "   0.          1.          0.        ]\n",
            " [ 0.          0.          0.          0.          0.          0.          0.\n",
            "   0.          0.          1.        ]]\n"
          ]
        }
      ],
      "source": [
        "max_num_states = 10\n",
        "\n",
        "def build_latent_state(num_states, max_num_states, daily_change_prob=0.05):\n",
        "\n",
        "  # Give probability exp(-100) ~= 0 to states outside of the current model.\n",
        "  initial_state_logits = -100. * np.ones([max_num_states], dtype=np.float32)\n",
        "  initial_state_logits[:num_states] = 0.\n",
        "\n",
        "  # Build a transition matrix that transitions only within the current\n",
        "  # `num_states` states.\n",
        "  transition_probs = np.eye(max_num_states, dtype=np.float32)\n",
        "  if num_states \u003e 1:\n",
        "    transition_probs[:num_states, :num_states] = (\n",
        "        daily_change_prob / (num_states-1))\n",
        "    np.fill_diagonal(transition_probs[:num_states, :num_states],\n",
        "                     1-daily_change_prob)\n",
        "  return initial_state_logits, transition_probs\n",
        "\n",
        "# For each candidate model, build the initial state prior and transition matrix.\n",
        "batch_initial_state_logits = []\n",
        "batch_transition_probs = []\n",
        "for num_states in range(1, max_num_states+1):\n",
        "  initial_state_logits, transition_probs = build_latent_state(\n",
        "      num_states=num_states,\n",
        "      max_num_states=max_num_states)\n",
        "  batch_initial_state_logits.append(initial_state_logits)\n",
        "  batch_transition_probs.append(transition_probs)\n",
        "\n",
        "batch_initial_state_logits = np.array(batch_initial_state_logits)\n",
        "batch_transition_probs = np.array(batch_transition_probs)\n",
        "print(\"Shape of initial_state_logits: {}\".format(batch_initial_state_logits.shape))\n",
        "print(\"Shape of transition probs: {}\".format(batch_transition_probs.shape))\n",
        "print(\"Example initial state logits for num_states==3:\\n{}\".format(batch_initial_state_logits[2, :]))\n",
        "print(\"Example transition_probs for num_states==3:\\n{}\".format(batch_transition_probs[2, :, :]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "k9NMBMBq2UQw"
      },
      "source": [
        "Now we proceed similarly as above. This time we'll use an extra batch dimension in `trainable_rates` to separately fit the rates for each model under consideration."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "Ok-3Nzt1suyw"
      },
      "outputs": [],
      "source": [
        "tf.reset_default_graph()\n",
        "\n",
        "trainable_rates = tf.exp(tf.get_variable(\n",
        "    'log_rates',\n",
        "    initializer=(\n",
        "        np.log(np.mean(observed_counts)) *\n",
        "        np.ones([batch_initial_state_logits.shape[0], max_num_states]) +\n",
        "        tf.random_normal([1, max_num_states]))))\n",
        "\n",
        "hmm = tfd.HiddenMarkovModel(\n",
        "  initial_distribution=tfd.Categorical(\n",
        "      logits=batch_initial_state_logits),\n",
        "  transition_distribution=tfd.Categorical(probs=batch_transition_probs),\n",
        "  observation_distribution=tfd.Poisson(trainable_rates),\n",
        "  num_steps=len(observed_counts))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "eC5vFBX12PvA"
      },
      "source": [
        "In computing the total log prob, we are careful to sum over only the priors for the rates actually used by each model component:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "ly0mT_mqdubx"
      },
      "outputs": [],
      "source": [
        "rate_prior = tfd.LogNormal(5, 5)\n",
        "\n",
        "prior_lps = rate_prior.log_prob(trainable_rates)\n",
        "prior_lp = tf.stack([\n",
        "    tf.reduce_sum(prior_lps[i, :i+1])\n",
        "    for i in range(max_num_states)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "PR5zL24UDkPW"
      },
      "outputs": [],
      "source": [
        "total_log_prob = (\n",
        "    prior_lp +\n",
        "    hmm.log_prob(observed_counts))\n",
        "\n",
        "train_op = tf.train.AdamOptimizer(0.1).minimize(-total_log_prob)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "yPqvJ9TS5F98"
      },
      "source": [
        "Now we optimize the *batch* objective we've constructed, fitting all candidate models simultaneously:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 417
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 5825,
          "status": "ok",
          "timestamp": 1549416951113,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "hAb22rYe1K_O",
        "outputId": "b3526e8a-6db1-46e5-c09e-81eeaa1bec3f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "step 0: log probs [-944.81622314 -951.96899414 -311.06134033 -290.16409302 -297.9737854\n",
            " -296.89675903 -297.65429688 -303.55670166 -308.96862793 -315.28204346]\n",
            "step 20: log probs [-865.35925293 -346.33798218 -254.09133911 -252.77212524 -258.06970215\n",
            " -265.33407593 -270.79260254 -276.39294434 -279.54763794 -284.41073608]\n",
            "step 40: log probs [-843.74700928 -300.53594971 -252.47946167 -248.60549927 -255.34309387\n",
            " -261.86502075 -267.58944702 -273.53823853 -277.72042847 -282.41430664]\n",
            "step 60: log probs [-843.8303833  -297.07070923 -251.62112427 -248.37060547 -254.37657166\n",
            " -261.41439819 -267.38128662 -273.18225098 -277.34545898 -281.02520752]\n",
            "step 80: log probs [-843.47076416 -296.8565979  -251.4430542  -248.34793091 -254.26861572\n",
            " -261.33291626 -267.31582642 -273.15090942 -277.22296143 -280.6852417 ]\n",
            "step 100: log probs [-843.45892334 -296.71670532 -251.42454529 -248.34436035 -254.25704956\n",
            " -261.32034302 -267.30819702 -273.14572144 -277.128479   -280.58456421]\n",
            "step 120: log probs [-843.45825195 -296.50662231 -251.42242432 -248.34327698 -254.25439453\n",
            " -261.31921387 -267.30773926 -273.14489746 -277.02880859 -280.48141479]\n",
            "step 140: log probs [-843.45837402 -296.17553711 -251.42218018 -248.34336853 -254.25418091\n",
            " -261.31881714 -267.30752563 -273.14498901 -276.92071533 -280.37225342]\n",
            "step 160: log probs [-843.45794678 -295.9664917  -251.42224121 -248.34350586 -254.25415039\n",
            " -261.31890869 -267.30755615 -273.1449585  -276.80557251 -280.25689697]\n",
            "step 180: log probs [-843.45819092 -295.96182251 -251.4223938  -248.34361267 -254.25410461\n",
            " -261.31893921 -267.30755615 -273.14498901 -276.68444824 -280.13562012]\n",
            "step 200: log probs [-843.45843506 -295.95932007 -251.42214966 -248.34326172 -254.25422668\n",
            " -261.31890869 -267.30752563 -273.14489746 -276.55825806 -280.00921631]\n"
          ]
        }
      ],
      "source": [
        "sess = tf.Session()\n",
        "sess.run(tf.global_variables_initializer())\n",
        "\n",
        "for step in range(201):\n",
        "  _, total_log_prob_ = sess.run((train_op, total_log_prob))\n",
        "\n",
        "  total_log_prob_ = sess.run((total_log_prob))\n",
        "  if step % 20 == 0:\n",
        "    print(\"step {}: log probs {}\".format(step, total_log_prob_))\n",
        "\n",
        "learned_rates_ = sess.run(trainable_rates)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 325
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 859,
          "status": "ok",
          "timestamp": 1549416766980,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "_Jsthql_IxhW",
        "outputId": "496a5749-c4c9-4a37-b2d7-94efd3569cd3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "\u003cmatplotlib.text.Text at 0x7fac240e5f90\u003e"
            ]
          },
          "execution_count": 75,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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EcJSMCCGECI6SESGEEMFRMiKEECI4SkaEEEIER8mIEEKI4CgZEUIIERwlI0IIIYKjZEQI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\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fac2247ed90\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "num_states = np.arange(1, max_num_states+1)\n",
        "plt.plot(num_states, total_log_prob_)\n",
        "plt.ylim([-400, -200])\n",
        "plt.ylabel(\"marginal likelihood $\\\\tilde{p}(x)$\")\n",
        "plt.xlabel(\"number of latent states\")\n",
        "plt.title(\"Model selection on latent states\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Kq7SKiR-6c1l"
      },
      "source": [
        "Examining the likelihoods, we see that the (approximate) marginal likelihood prefers a three- or four-state model (the specific ordering may vary between runs of this notebook). This seems quite plausible -- the 'true' model had four states, but from just looking at the data it's hard to rule out a three-state explanation.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "u0tqU6Lo6pFD"
      },
      "source": [
        "We can also extract the rates fit for each candidate model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 290
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 290,
          "status": "ok",
          "timestamp": 1549417295261,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "lnXTiGX4d6e4",
        "outputId": "2e5083b8-e06f-4c1f-a220-7fff9048d8d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "rates for 1-state model: [ 35.47252655]\n",
            "rates for 2-state model: [ 49.80340958   5.5464735 ]\n",
            "rates for 3-state model: [ 51.70901871  21.54446411   3.85725999]\n",
            "rates for 4-state model: [ 54.10387039  41.0456543    3.85735941  20.81387329]\n",
            "rates for 5-state model: [ 20.70896912  41.14213943   3.85735488  20.7090435   54.099823  ]\n",
            "rates for 6-state model: [ 40.84007645  54.07774353   3.8573699   20.73516846  54.07864761\n",
            "  40.84005737]\n",
            "rates for 7-state model: [ 54.03517532  20.62760925   3.85736728  20.62771606  54.03516388\n",
            "  41.00030136  54.03511047]\n",
            "rates for 8-state model: [ 54.03466034  20.49001694   3.85736227  20.48946953  54.03440094\n",
            "  41.05328751  54.03466034  20.48965836]\n",
            "rates for 9-state model: [ 54.03406525  20.4622879    0.77144349  20.46162796  54.03421021\n",
            "  41.01068115  54.03433609  20.4616375    3.86175203]\n",
            "rates for 10-state model: [ 40.89097595  20.43305016   0.76783544  20.43265152  54.06986618\n",
            "  40.890625    54.06981659   3.8186841    3.81822777  20.43225288]\n"
          ]
        }
      ],
      "source": [
        "for i, learned_model_rates_ in enumerate(learned_rates_):\n",
        "  print(\"rates for {}-state model: {}\".format(i+1, learned_model_rates_[:i+1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "8eArj7lke9Ei"
      },
      "source": [
        "And plot the explanations each model provides for the data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {},
        "colab_type": "code",
        "id": "XEuhytSKcn4g"
      },
      "outputs": [],
      "source": [
        "posterior_probs_ = sess.run(\n",
        "    hmm.posterior_marginals(observed_counts).probs_parameter())\n",
        "most_probable_states = np.argmax(posterior_probs_, axis=-1)\n",
        "sess.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 0,
      "metadata": {
        "colab": {
          "height": 873
        },
        "colab_type": "code",
        "executionInfo": {
          "elapsed": 3209,
          "status": "ok",
          "timestamp": 1549417525286,
          "user": {
            "displayName": "",
            "photoUrl": "",
            "userId": ""
          },
          "user_tz": 480
        },
        "id": "g3RiZCjzuL8o",
        "outputId": "b0cc60cc-e0ac-4a40-ea4e-0ed69562d6b6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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pz8/Q0BCczkXMz89iZGRr0ddJmzB6eopX4litNrhcLsRisYKgXCIRR2urqOek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\nl2w2g8lkIpcTj/0mXdVMlbcKv9+HyWSqNHkzGAyMjIzh861z4sRcUzskGt2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td2tE/u72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WxZ5e81FotisVhaln0eJaptzJMnT1fWrehgNLoNtB5jp2zoD9qx2dEm/Wd/9mf5gz/4A55/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\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4gCAADUfSKT719fUIBoNRbSaTSVYLZBIRERHRyNdnkq5QKHDvvffiiy++OK8LlZeX4/Dh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\nkQChJF1EREREREQkQPx/xvn+I5znKXcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "\u003cmatplotlib.figure.Figure at 0x7fac1f436410\u003e"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(14, 12))\n",
        "for i, learned_model_rates_ in enumerate(learned_rates_):\n",
        "  ax = fig.add_subplot(4, 3, i+1)\n",
        "  ax.plot(learned_model_rates_[most_probable_states[i]], c='green', lw=3, label='inferred rate')\n",
        "  ax.plot(observed_counts, c='black', alpha=0.3, label='observed counts')\n",
        "  ax.set_ylabel(\"latent rate\")\n",
        "  ax.set_xlabel(\"time\")\n",
        "  ax.set_title(\"{}-state model\".format(i+1))\n",
        "  ax.legend(loc=4)\n",
        "plt.tight_layout()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "sw25-htzfxLZ"
      },
      "source": [
        "It's easy to see how the one-, two-, and (more subtly) three-state models provide inadequate explanations. Interestingly, all models above four states provide essentially the same explanation! This is likely because our 'data' is relatively clean and leaves little room for alternative explanations; on messier real-world data we would expect the higher-capacity models to provide progressively better fits to the data, with some tradeoff point where the improved fit is outweighted by model complexity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "fY5E0BaPI7lz"
      },
      "source": [
        "### Extensions\n",
        "\n",
        "The models in this notebook could be straightforwardly extended in many ways. For example:\n",
        "\n",
        "- allowing latent states to have different probabilities (some states may be common vs rare)\n",
        "- allowing nonuniform transitions between latent states (e.g., to learn that a machine crash is usually followed by a system reboot is usually followed by a period of good performance, etc.)\n",
        "- other emission models, e.g. `NegativeBinomial` to model varying dispersions in count data, or continous distributions such as `Normal` for real-valued data.\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "-o9zA5TO_-hx"
      ],
      "name": "Multiple changepoint detection and Bayesian model selection",
      "provenance": [
        {
          "file_id": "1Wa4oGxkHrb7toU-pdFhMJi2PAYmvVFBT",
          "timestamp": 1543641172993
        }
      ],
      "version": "0.3.2"
    },
    "kernelspec": {
      "display_name": "Python 2",
      "name": "python2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
